Soil profile analysis using interactive visualizations, machine learning, and deep learning

• Software requirements for analyzing soil profiles and predicting properties. A set of common interactive 2D 3D visualizations chemical measurement data. Machine learning deep approaches Deep model pH H 2 O KCl from Vis–NIR spectra. Soil is an essential element life, properties are crucial in health. Recent developments proximal sensor technologies, such as portable X-ray fluorescence (pXRF) spectroscopy or visible near-infrared (Vis–NIR) spectroscopy, offer rapid non-destructive alternatives quantifying data profiles. While the collection time using these technologies decreases significantly, subsequent analysis remains time-consuming, current solutions only provide basic visualizations. Furthermore, use collected sensors to predict high-level has garnered worldwide attention past decade, owing its convenience. Therefore, this paper discusses objectives software area, consolidated interviewing 102 stakeholders. Following requirements, visualizers work closely with scientists propose a pXRF These receive positive feedback domain experts. This project also explores various machine spectral then proposes called RDNet that achieves state-of-the-art results spectra acquired globally distributed samples.